Artificial Intelligence in Medical Education: Best Practices Using Machine Learning to Assess Surgical Expertise in Virtual Reality Simulation
| Autor: | Rolando F. Del Maestro, Hamed Azarnoush, Nicole Ledwos, Alexander Winkler-Schwartz, Vincent Bissonnette, Recai Yilmaz, Samaneh Siyar, Nykan Mirchi, Nirros Ponnudurai, Bekir Karlik |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
Best practice media_common.quotation_subject Virtual reality Machine learning computer.software_genre Field (computer science) Education Machine Learning 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Quality (business) 030212 general & internal medicine Simulation Training media_common Psychomotor learning business.industry Virtual Reality Checklist Systematic review Education Medical Graduate 030220 oncology & carcinogenesis General Surgery Practice Guidelines as Topic Surgery Artificial intelligence Clinical Competence Educational Measurement business Knowledge transfer computer |
| Zdroj: | Journal of surgical education. 76(6) |
| ISSN: | 1878-7452 |
| Popis: | Objective Virtual reality simulators track all movements and forces of simulated instruments, generating enormous datasets which can be further analyzed with machine learning algorithms. These advancements may increase the understanding, assessment and training of psychomotor performance. Consequently, the application of machine learning techniques to evaluate performance on virtual reality simulators has led to an increase in the volume and complexity of publications which bridge the fields of computer science, medicine, and education. Although all disciplines stand to gain from research in this field, important differences in reporting exist, limiting interdisciplinary communication and knowledge transfer. Thus, our objective was to develop a checklist to provide a general framework when reporting or analyzing studies involving virtual reality surgical simulation and machine learning algorithms. By including a total score as well as clear subsections of the checklist, authors and reviewers can both easily assess the overall quality and specific deficiencies of a manuscript. Design The Machine Learning to Assess Surgical Expertise (MLASE) checklist was developed to help computer science, medicine, and education researchers ensure quality when producing and reviewing virtual reality manuscripts involving machine learning to assess surgical expertise. Setting This study was carried out at the McGill Neurosurgical Simulation and Artificial Intelligence Learning Centre. Participants The authors applied the checklist to 12 articles using machine learning to assess surgical expertise in virtual reality simulation, obtained through a systematic literature review. Results Important differences in reporting were found between medical and computer science journals. The medical journals proved stronger in discussion quality and weaker in areas related to study design. The opposite trends were observed in computer science journals. Conclusions This checklist will aid in narrowing the knowledge divide between computer science, medicine, and education: helping facilitate the burgeoning field of machine learning assisted surgical education. |
| Databáze: | OpenAIRE |
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